资源列表
[数值算法/人工智能] shulitongji
说明:数理统计程序集,包含最基本的经典统计程序,数据处理程序-mathematical statistics collection procedures, including basic classical statistical procedures, data processing procedures<张进> 在 2008-10-13 上传 | 大小:2.13mb | 下载:0
[数值算法/人工智能] shulitongji
说明:数理统计程序集,包含最基本的经典统计程序,数据处理程序-mathematical statistics collection procedures, including basic classical statistical procedures, data processing procedures<张进> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[数学计算/工程计算] optpp-2.4.tar
说明:OPT++,Sandia开发的一套优化工具包。可以实现经典的q-Newton等基于梯度的优化算法!-OPT++, Sandia developed a set of optimization toolkit. Can realize the classical q-Newton, such as gradient-based optimization algorithm!<shenxu> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[matlab例程] Statistical-Process-Control-5E
说明:This about Statistical process control.-This is about Statistical process control.<godswen> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[matlab例程] TestFunctionsForOptimization
说明:This paper provides the review of literature benchmarks (test functions) commonly used in order to test optimization procedures dedicated for multidimensional, continuous optimization task. Special attention has been paid to multiple-extreme func<omid> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[人工智能/神经网络/遗传算法] functions
说明:This paper provides the review of literature benchmarks (test functions) commonly used in order to test optimization procedures dedicated for mul- tidimensional, continuous optimization task. Special attention has been paid to multiple-extreme<payal> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[数学计算/工程计算] superlu_4.1.tar
说明:开源的superLU方法,高效实用,同时提供了s说明性文章和多种语言的代码-Open source superLU method is efficient and practical, while providing the code of s descr iptive articles and multilingual<BoLiu> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[matlab例程] IUST-v23n1p43-fa
说明:this about risk managment problems-this is about risk managment problems....<mostafa> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0
[matlab例程] sim
说明:Another way to represent a polynomial is to use the Laplace variable s within MATLAB. This method is mainly used throughout these tutorials. Let's ignore the details of the Laplace domain for now and just represent polynomials with the s variable. To<sandikeren> 在 2025-11-17 上传 | 大小:2.13mb | 下载:0